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Most-download articles are from the articles published in 2022 during the last three month.

Original Papers
Quantitative Study of Butterfly Diversity in Wando Quercus acuta Forest Over 5 Years (2017-2021)
Sanghun Lee, Na-Hyun Ahn
GEO DATA. 2023;5(2):55-59.   Published online June 20, 2023
DOI: https://doi.org/10.22761/GD.2023.0010
  • 1,724 View
  • 283 Download
AbstractAbstract PDF
This study presents the long-term quantitative data on butterflies in Wando Arboretum, which represents the only warm-temperate forest located in the southernmost part of South Korea. This arboretum has significant academic value as approximately 770 species of rare woody plants or herbs, such as the Japanese evergreen oak (Quercus acuta), found in warm temperate zones grow under natural conditions here. In this project, the butterflies in this region were studied due to their sensitivity to temperature changes. The study was conducted from March-April to October-November over 5 years (2017-2021) in the region dominated by Japanese evergreen oak. We found 1,743 individuals of 47 butterfly species belonging to five families. The acquired butterfly data could serve as a reference for the further development of a network-oriented database for assessing temporal climate changes.
High-Resolution Bioclimatic Variables in Mt. Jirisan and Hallasan under Climate Change Scenario
Sanghun Lee, Seungbum Hong, Kyungeun Lee
GEO DATA. 2023;5(4):314-320.   Published online December 20, 2023
DOI: https://doi.org/10.22761/GD.2023.0039
  • 514 View
  • 68 Download
AbstractAbstract PDF
Many endemic and rare species live in Korea’s subalpine zone, but there have been many research results showing that alpine creatures are disappearing due to recent climate change. Therefore, in this study, bioclimatic variables with 100 m resolution were created for Mt. Jirisan and Mt. Hallasan, representative mountainous regions in Korea. Nineteen high-resolution bioclimatic variables were created for the current and 4 future periods, and the generated data is believed to represent topographical characteristics well. This data is expected to be useful to predict potential habitats through species distribution modeling and the impact of climate change on organisms limited to alpine regions.
Review
Data used for GIS-based Flood Susceptibility Mapping
Saro Lee, Fatemeh Rezaie
GEO DATA. 2022;4(1):1-15.   Published online March 31, 2022
DOI: https://doi.org/10.22761/DJ2022.4.1.001
  • 765 View
  • 87 Download
AbstractAbstract PDF
The dramatic increase in flood incidents as a significant threat to human life and property, environment, and infrastructure indicates the necessity of mapping spatial distribution of flood susceptible areas to reduce destructive effects of flooding. During the last decade, the integration of the geographic information system (GIS) with the remote sensing data provide efficient means to generate a more reliable and precise flood susceptibility map. The present study contains a review of 200 articles on the application of GIS-based methods in indicating flood vulnerable areas. The papers were reviewed in terms of influential variables, study area, and the number of articles published in the last 10 years. The review shows that the number of studies has increased since 2012. The total study areas covered 39 countries that were mostly located in Asia where the major developments and infrastructures have been constructed in the floodplains. The most common study areas was Iran (44 articles, 22%), followed by India (26 articles, 13%), China (26 articles, 11%), and Vietnam (15 articles, 7.5%). More than 90 variables were considered to map flood susceptible areas that the top 5 widely used flood conditioning factor are slope (98% of total articles), followed by elevation (92% of total articles), land use/land cover (79.5% of total articles), distance to the river (76.5% of total articles), and rainfall (73% of total articles). The review implies that many natural and anthropogenic factors affect flooding and the combination of both groups of factors is necessary to accurately detect and map flood-prone parts of the study area.
Original Paper
Construction of Exploration Data for Greenhouse Gas Geologic Storage: Focusing on Geological Cross-section Data
Bokyun Ko, Sungjae Park, Saro Lee
GEO DATA. 2023;5(3):222-229.   Published online September 26, 2023
DOI: https://doi.org/10.22761/GD.2023.0023
  • 562 View
  • 64 Download
AbstractAbstract PDF
In this study, the most basic data, underground geological structure data, that is, geological cross-section data, were established to select a candidate site for underground storage of greenhouse gases based on AI. As a target area, the Gyeongsang Basin, where a large amount of sedimentary rocks are distributed, was selected as the greenhouse gas can be stored most effectively in sedimentary rocks. To this end, the acquisition and edit step, the refinement step, and the labeling step were carried out in the order of raw data collection, source data and labeling data construction to construct the geological cross-section data. This data can be downloaded through the AI hub site (https://aihub.or.kr/aihubdata/data/view.do?curr Menu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71390) operated by the Korea Institute for Intelligent Information Society Promotion.
Review Paper
Global Geospatial Data for Flood and Landslide Susceptibility Mapping
Saro Lee, Rezaie Fatemeh
GEO DATA. 2023;5(4):380-393.   Published online December 28, 2023
DOI: https://doi.org/10.22761/GD.2023.0058
  • 280 View
  • 47 Download
AbstractAbstract PDF
Susceptibility mapping is an important component of natural hazard risk assessment and management. Susceptibility maps for floods and landslides, which are particularly damaging to human life and property, can provide a comprehensive understanding of risk areas and factors related to flood and landslide susceptibility. To create a global flood and landslide susceptibility map, global geospatial data for 37,984 landslide and 6,682 flood locations, as well as 11 selected environmental factors were used to construct a geographic information system database. The 11 environmental factors found to influence flood and landslide occurrence were rainfall, slope, terrain position index, plane curvature, terrain wetness index, distance from rivers, land use, soil texture, soil moisture, geology, and temperature. These data were then used directly to create a global flood and landslide susceptibility map.
Original Papers
Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data
Jaese Lee, Dukwon Bae, Young Jun Kim, Jungho Im
GEO DATA. 2023;5(3):119-125.   Published online September 25, 2023
DOI: https://doi.org/10.22761/GD.2023.0025
  • 557 View
  • 60 Download
AbstractAbstract PDF
Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.
The Study of Distribution for the Flora of Alien Species and Ecosystem Disturbing Species on Coastal Sand Dune in Chungcheong to Jeolla Region, South Korea
Seonghun Lee, Jihyun Kang, Hyun-Su Hwang
GEO DATA. 2023;5(4):262-272.   Published online December 20, 2023
DOI: https://doi.org/10.22761/GD.2023.0031
  • 275 View
  • 27 Download
AbstractAbstract PDF
This study was conducted to provide the coastal sand dunes flora of vascular plants in Chungcheong to Jeolla region based national coastal dune natural environment survey from 2018 to 2019. In the study area, a total 631 taxa, consisting of 119 family, 372 genera, 566 species, 8 subspecies, 50 varieties, and 7 forma, were found. Among them, there were 95 taxa with 23 family, 66 genera, 99 species and 5 varieties as alien species. The number of alien species ranged from 7 to 45 on each coastal sand dune. The largest number was recorded in Sinjimyeongsa dune, while the lowest was in Namujeon dune. Moreover, ecosystem disturbing species had mainly existed on Sinhap dune. Japanese hop (Humulus japonicus) were distributed most widely on 17 coastal sand dune, and bur cucumber (Sicyos angulatus) was only found on Sinhap dune. The spatial status of flora of coastal sand dune in our data can be basic ecological information for the conservation and management of the coastal dune plant species diversity.
GeoAI Dataset for Industrial Park and Quarry Classification from KOMPSAT-3/3A Optical Satellite Imagery
Che-Won Park, Hyung-Sup Jung, Won-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang, Moung-jin Lee, Geun-Hyouk Han, Il-Hoon Choi
GEO DATA. 2023;5(4):238-243.   Published online December 28, 2023
DOI: https://doi.org/10.22761/GD.2023.0052
  • 231 View
  • 25 Download
AbstractAbstract PDF
Air pollution is a serious problem in the world, and it is necessary to monitor air pollution emission sources in other neighboring countries to respond to the problem of air pollution spreading across borders. In this study, we utilized domestic and international optical images from KOMPSAT-3/3A satellites to build an AI training dataset for classifying industrial parks and quarries, which are representative sources of air pollution emissions. The data can be used to identify the distribution of air pollution emission sources located at home and abroad along with various state-of-the-art models in the image segmentation field, and is expected to contribute to the preservation of Korea’s air environment as a basis for establishing air-related policies.
The Cheonji Lake GeoAI Dataset Based in Synthetic Aperture Radar Images: TerraSAR-X, Sentinel-1 and ALOS PALSAR-2
Eu-Ru Lee, Ha-Seong Lee, Ji-Min Lee, Sun-Cheon Park, Hyung-Sup Jung
GEO DATA. 2023;5(4):251-261.   Published online December 29, 2023
DOI: https://doi.org/10.22761/GD.2023.0056
  • 261 View
  • 23 Download
AbstractAbstract PDF
The fluctuations in the area and level of Cheonji in Baekdu Mountain have been employed as significant indicators of volcanic activity. Monitoring these changes directly in the field is challenging because of the geographical and spatial features of Baekdu Mountain. Therefore, remote sensing technology is crucial. Synthetic aperture radar utilizes high-transmittance microwaves to directly emit and detect the backscattering from objects. This weatherproof approach allows monitoring in every climate. Additionally, it can accurately differentiate between water bodies and land based on their distinct roughness and permittivity characteristics. Therefore, satellite radar is highly suitable for monitoring the water area of Cheonji. The existing algorithms for classifying water bodies using satellite radar images are significantly impacted by speckle noise and shadows, resulting in frequent misclassification. Deep learning techniques are being utilized in algorithms to accurately compute the area and boundary of interest in an image, surpassing the capabilities of previous algorithms. This study involved the creation of an AI dataset specifically designed for detecting water bodies in Cheonji. The dataset was constructed using satellite radar images from TerraSAR-X, Sentinel-1, and ALOS-2 PALSAR-2. The primary objective was to accurately detect the area and level of water bodies. Applying the dataset of this study to deep learning techniques for ongoing monitoring of the water bodies and water levels of Cheonji is anticipated to significantly contribute to a systematic method for monitoring and forecasting volcanic activity in Baekdu Mountain.
GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery
Sung-Hyun Gong, Hyung-Sup Jung, Moung-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang
GEO DATA. 2023;5(4):231-237.   Published online December 28, 2023
DOI: https://doi.org/10.22761/GD.2023.0054
  • 388 View
  • 23 Download
AbstractAbstract PDF
In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.
Investigation of Wildlife Crossing Structures in South Korea
Euigeun Song, Sooahn Heo, Il Ryong Kim, Sehee Kim, Hanbi Lee
GEO DATA. 2023;5(4):273-276.   Published online December 22, 2023
DOI: https://doi.org/10.22761/GD.2023.0041
  • 213 View
  • 21 Download
AbstractAbstract PDF
Roads, railways and infrastructure are constructed with consideration of their environmental impacts, especially habitat fragmentation. Wildlife crossing structures increase the permeability of roads and other linear infrastructures for wildlife by allowing animals to safely cross under or over roads and by reducing the risk of wildlife-vehicle collosions. We investigated the location and type of 564 wildlife crossing structures in South Korea. Between April and October 2023, we identified 365 overpasses and 199 underpasses of wildlife crossing structures respectively. Gyeonggi-do and Gyeongsangbuk-do had the largest number of wildlife crossing structures. This study can provide basic information for the effective management of wildlife crossing structures.
A Study on C-band Synthetic Aperture Radar Soil Moisture Estimation Based on Machine Learning Using Soil Physics, Topography, and Hydrological Information
Jeehun Chung, Yonggwan Lee, Jinuk Kim, Wonjin Jang, Seongjoon Kim
GEO DATA. 2023;5(3):137-146.   Published online September 22, 2023
DOI: https://doi.org/10.22761/GD.2023.0026
  • 438 View
  • 40 Download
AbstractAbstract PDF
In this study, we applied machine learning to estimate soil moisture levels in South Korea by harnessing data from the Sentinel-1 C-band synthetic aperture radar (SAR). Our approach incorporated not only the relationship between backscattering coefficients and soil moisture but also diverse physical characteristics. This encompassed topographic information, soil physics data, and antecedent precipitation which is a hydrological factor influencing the initial condition of soil moisture. We applied a variety of machine-learning techniques and conducted a comprehensive analysis to compare the performance of each model.
BRDF Data for Coniferous Forests Acquired from Multispectral Camera Onboard a Unmanned Aerial Vehicle
Seungil Baek, Sooyoon Koh, Jong Hyuk Lee, Wonkook Kim
GEO DATA. 2023;5(4):371-379.   Published online December 28, 2023
DOI: https://doi.org/10.22761/GD.2023.0057
  • 147 View
  • 18 Download
AbstractAbstract PDF
Bidirectional reflectance distribution function (BRDF) is a distribution of directional reflectance for varying viewing and solar geometry. BRDF of a target is important in processing optical image data from satellites, because the observed radiance has great dependency on the direction (or angle) of reflection. It is desirable that the BRDF of any targets is characterized for rigorous BRDF correction of satellite data, since the sun-sensor-target geometry of satellites often varies in a very limited range, limiting the full characterization of target BRDF. This study provides BRDF data set for typical coniferous forests in Korea, by using a multispectral camera onboard a unmanned aerial vehicle (UAV). By operating the UAV in a goniometer-like way, reflectance data for all possible viewing zenith and azimuth angles were obtained. The BRDF data collected from the 3 campaigns in different days were visualized in a polar-coordinate, together with the standard deviation calculated for each zenith/azimuth bin made in 1˚ interval. The data sets demonstrated reflectance distribution over the wide range of angles with sound data quality, suggesting commonly known BRDF characteristics for forests such as strong back-scattering and hot spot area in the viewing zenith angle near the solar zenith angle. This data set is expected to be utilized for the BRDF correction of various satellites including Agro-forest satellite of Korea which is to be launched in 2025 that has similar spectral bands with the ones used in this study.
GeoAI Dataset for Training Deep Learning-Based Optical Satellite Image Matching Model
Jin-Woo Yu, Che-Won Park, Hyung-Sup Jung
GEO DATA. 2023;5(4):244-250.   Published online December 28, 2023
DOI: https://doi.org/10.22761/GD.2023.0048
  • 226 View
  • 15 Download
AbstractAbstract PDF
Satellite imagery is being used to monitor the Earth, as it allows for the continuous provision of multi-temporal observations with consistent quality. To analyze time series remote sensing data with high accuracy, the process of image registration must be conducted beforehand. Image registration techniques are mainly divided into region-based registration and feature-based registration, and both techniques extract the same points based on the similarity of spectral characteristics and object shapes between master and slave images. In addition, recently, deep learning-based siamese neural network and convolutional neural network models have been utilized to match images. This has high performance compared to previous non-deep learning algorithms, but a very large amount of data is required to train a deep learning-based image registration model. In this study, we aim to generate a dataset for training a deep learning-based optical image registration model. To build the data, we acquired Satellite Side-Looking (S2Looking) data, an open dataset, and performed preprocessing and data augmentation on the data to create input data. After that, we added offsets to the X and Y directions between the master and slave images to create label data. The preprocessed input data and labeled data were used to build a dataset suitable for image registration. The data is expected to be useful for training deep learning-based satellite image registration models.
Research on Tree Distribution in the Mt. Hambeak Area of Taebaek City Using Hyperspectral Image-LiDAR in 2019-2020
Seung Won Lee, Junghyun Lee, Nam-Shin Kim
GEO DATA. 2023;5(4):330-338.   Published online December 27, 2023
DOI: https://doi.org/10.22761/GD.2023.0046
  • 97 View
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AbstractAbstract PDF
This research was conducted to improve the vegetation survey method using hyperspectral imaging and LiDAR techniques. Using Ocean FX, spectral data of seven representative species of Mount Hambaek were acquired, and hyperspectral image data of Mount Hambaek were acquired using AisaFENIX 1K and microCASI-1920 sensors. For spectral data and hyperspectral image data, tree species data were extracted using the Spectral Angle Mapper (SAM) technique, and data such as tree species location, height, and diameter at breast height were extracted through LiDAR data. the results of an investigation A total of 39,351 trees were surveyed in the Mount Hambuk area, with 25,930 trees (65.9%) in Quercus mongolica, followed by Larix kaempferi with 6,805 trees (17.3%), Alnus sibirica with 3,625 trees (9.2%), Pinus dendiflora 1,764 trees (4.5%), Pinus koraiensis 605 trees (1.5%), Pinus rigida 405 trees (1.0%), and Betulaermanii 217 trees (0.5%), As a result of selecting 28 representative colonies to be surveyed and conducting on-site verification, 27 out of 28 colonies were found to be 96.43% accurate.

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